#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size RANK_SIZE
# 网络名称，同目录名称
Network="InceptionV3_ID1596_for_PyTorch"
# 训练batch_size
batch_size=2048
# 训练使用的npu卡数
export RANK_SIZE=8
# 数据集路径,保持为空,不需要修改
data_path=""

# 训练epoch
train_epochs=240
# 学习率
learning_rate=1.2

# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --train_epochs* ]];then
        train_epochs=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --batch_size* ]];then
        batch_size=`echo ${para#*=}`
    fi
done

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi


###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_dirname=${cur_path##*/}
if [ x"${cur_path_last_dirname}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
ASCEND_DEVICE_ID=0
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi


#################启动训练脚本#################
# 训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi

KERNEL_NUM=$(($(nproc)/8))
for i in $(seq 0 7)
do
  if [ $(uname -m) = "aarch64" ]
  then
    PID_START=$((KERNEL_NUM*i))
    PID_END=$((PID_START + KERNEL_NUM - 1))
    taskset -c $PID_START-$PID_END python3 ./main.py \
      -a inception_v3 \
      --amp \
      --loss_scale=128 \
      --data ${data_path} \
      --addr=$(hostname -I |awk '{print $1}') \
      --seed=49 \
      --data_shuffle True \
      --workers=128 \
      --learning-rate=${learning_rate} \
      --mom=0.9 \
      --weight-decay=1.0e-04  \
      --print-freq=30 \
      --dist-url='tcp://127.0.0.1:50000' \
      --dist-backend='hccl' \
      --multiprocessing-distributed \
      --world-size=1 \
      --rank=0 \
      --device='npu' \
      --gpu=${i}  \
      --epochs=${train_epochs} \
      --label-smoothing=0.1 \
      --batch-size=${batch_size} > ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
  else
    python3 ./main.py \
      -a inception_v3 \
      --amp \
      --loss_scale=128 \
      --data ${data_path} \
      --addr=$(hostname -I |awk '{print $1}') \
      --seed=49 \
      --data_shuffle True \
      --workers=128 \
      --learning-rate=${learning_rate} \
      --mom=0.9 \
      --weight-decay=1.0e-04  \
      --print-freq=30 \
      --dist-url='tcp://127.0.0.1:50000' \
      --dist-backend='hccl' \
      --multiprocessing-distributed \
      --world-size=1 \
      --rank=0 \
      --device='npu' \
      --gpu=${i}  \
      --epochs=${train_epochs} \
      --label-smoothing=0.1 \
      --batch-size=${batch_size} > ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
  fi
done
wait

# 训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

# 结果打印，不需要修改
echo "------------------ Final result ------------------"
# 输出性能FPS，需要模型审视修改
FPS=`grep "FPS@all" ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $7}' | tail -1`
# 打印，不需要修改
echo "Final Performance images/sec : $FPS"

# 输出训练精度,需要模型审视修改
train_accuracy=`grep -a '* Acc@1' ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print}'|awk -F "Acc@1" '{print $NF}'|awk -F " " '{print $1}'`

# 打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

# 性能看护结果汇总
# 训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'

# 获取性能数据，不需要修改
# 吞吐量
ActualFPS=${FPS}
# 单迭代训练时长
TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`

# 从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep Epoch: ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|grep -v Test|awk -F "Loss" '{print $NF}' | awk -F " " '{print $1}' >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

# 最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

# 关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
